共查询到19条相似文献,搜索用时 140 毫秒
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利用模糊神经网络建立煤炭评价模型,模型输出值作为中间结果,结合隶属度函数,最终确定煤炭质量级别.依据煤炭国标和行业标准,生成样本集,然后对样本集预处理,建立煤质评价模型,并以新疆某煤矿不同煤层深度的煤样为验证对象进行煤质评价.结果表明,评价模型的输出结果符合煤炭实际使用的情况.评价模型克服人为主观判断因素的不足,为目前存在的定性评价转为定量评价提供理论参考价值. 相似文献
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基于小波神经网络的中国能源需求预测模型 总被引:2,自引:0,他引:2
通过分析影响我国能源需求的主要因素,建立了基于小波神经网络的需求预测模型.采用定性与定量相结合的方式,分析了影响我国能源需求的主要因素,通过将人口总数、GDP、产业结构变化以及能源消费量的一阶滞后作为输入变量,建立基于小波神经网络的我国能源需求非线性预测模型.实验结果表明,该非线性预测模型与多元回归模型相比更加合理,具有更高的预测精度. 相似文献
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柯云泉 《数学的实践与认识》2007,37(2):67-74
对于一类细胞神经网络,以系统的输入、输出的反馈权值为参数,构成参数空间,引入几何方法,将参数空间分解分块成有限个区域,当系统参数在某一确定的区域上时,研究系统的输入—输出间关系,并给出输入、输出之间控制的一类判别方法. 相似文献
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建立随机环境下的定量指标评价模型、模糊随机环境下的定性指标评价模型和权重为模糊变量的综合评价模型,研究定量评价指标、定性评价指标分别为随机变量和模糊随机变量的企业综合实力评价模型和评价分析方法.最后,以制造业中HX行业的CA企业为例,实证模拟验证了模型的有效性和可操作性. 相似文献
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评价学生的创造力是一项极富挑战性的工作,创造力评价是培养开发创造力的首要环节.当需要评价的被试、创造力指标个数、指标等级水平都较多时,创造力评价工作便陷入了僵局,利用人工甚至无法完成.学习矢量量化(LVQ)神经网络在模式识别方面具有良好的性能,据此尝试用LVQ神经网络进行创造力评价.根据创造力评价指标及等级的数目,构建了由输入层、隐含层、输出层组成的LVQ神经网络,用训练好的网络对测试样本进行仿真测试,仿真结果和实际情况正好相符,体现出LVQ神经网络在创造力评价中的实用和有效性. 相似文献
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战场目标的识别是一个相当复杂的过程,为了实现识别的自动化和计算机化,采用BP神经网络方法构造数学模型,选取合适的输入、输出及隐性结点,通过反复的学习和测试就可以得到符合实际的结果,从而为指挥员判断敌情提供决策依据.选取常用的音响、闪光、烟尘、机动和配置五种目标特征信息作为输入结点,通过多次仿真测试,说明运用BP神经网络进行战场目标识别是可行的,这也为情报处理自动化系统的实现提供了一个可靠的方法. 相似文献
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L. C. W. Dixon 《Journal of Optimization Theory and Applications》2001,111(3):489-500
The generalization problem considered in this paper assumes that a limited amount of input and output data from a system is available, and that from this information an estimate of the output produced by another input is required. The ideas arose in the study of neural networks, but apply equally to any approximation approach. The main result is that the type of neural network to be used for generalization should be determined by the prior knowledge about the nature of the output from the system. Without such information, either of two networks matching the training data is equally likely to be the better at estimating the output generated by the same system at a new input. Therefore, the search for an optimum generalization network for use on all problems is inappropriate.For both (0, 1) and accurate real outputs, it is shown that simple approximations exist that fit the data, so these will be equally likely to generalize better than more sophisticated networks, unless prior knowledge is available that excludes them. For noisy real outputs, it is shown that the standard least squares approach forces the neural network to approximate an incorrect process; an alternative approach is outlined, which again is much easier to learn and use. 相似文献
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In this paper, a robust adaptive neural network synchronization controller is proposed for two chaotic systems with input time delay and uncertainty. The studied chaotic system may possess a wide class of nonlinear time-delayed input uncertainty. The radial basis function (RBF) neural network is used to approximate the unknown continuous bounded function item of the time delay uncertainty via appropriate weight value updated law. With the output of RBF neural network, a robust adaptive synchronization control scheme is presented for the time delay uncertain chaotic system. Finally, a simulation example is used to illustrate the effectiveness of the proposed synchronization control scheme. 相似文献
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We consider the inversion problem for linear systems, which involves estimation of the unknown input vector. The inversion
problem is considered for a system with a vector output and a vector input assuming that the observed output is of higher
dimension than the unknown input. The problem is solved by using a controlled model in which the control stabilizes the deviations
of the model output from the system output. The stabilizing model control or its averaged form may be used as the estimate
of the unknown system input.
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Translated from Nelineinaya Dinamika i Upravlenie, No. 4, pp. 17–22, 2004. 相似文献
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《Communications in Nonlinear Science & Numerical Simulation》2007,12(3):411-421
This paper presents a Volterra system-based nonlinear analysis of video-packet transmission over IP networks. With the Volterra system, which is applicable to the modeling of nonlinear dynamic systems from sets of input and output data, we applied a time-series analysis of measured data for network response evaluation. In a test-bed connected to the Internet, we measured two parameters: the time intervals between consecutive packets from a video server at the originating side, and the transmission time of packets between originating and terminating sides. We used these as input and output data for the Volterra system and confirmed that the relative error of this model changed with conditions of network systems, which suggested that the packet transmission process affected the degree of nonlinearity of the system. The proposed method can reproduce the time-series responses observed in video-packet transmission over the Internet, reflecting nonlinear dynamic behaviors such that the obtained results provided us with an effective depiction of network conditions at different times. 相似文献
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J S Liu W-M Lu C Yang M Chuang 《The Journal of the Operational Research Society》2009,60(11):1502-1510
Data envelopment analysis (DEA) is known to produce more than one efficient decision-making unit (DMU). This paper proposes a network-based approach for further increasing discrimination among these efficient DMUs. The approach treats the system under study as a directed and weighted network in which nodes represent DMUs and the direction and strength of the links represent the relative relationship among DMUs. In constructing the network, the observed node is set to point to its referent DMUs as suggested by DEA. The corresponding lambda values for these referent DMUs are taken as the strength of the network link. The network is weaved by not only the full input/output model, but also by models of all possible input/output combinations. Incorporating these models into the system basically introduces the merits of each DMU under various situations into the system and thus provides the key information for further discrimination. Once the network is constructed, the centrality concept commonly used in social network analysis—specifically, eigenvector centrality—is employed to rank the efficient DMUs. The network-based approach tends to rank high the DMUs that are not specialized and have balanced strengths. 相似文献
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This paper considers an adaptive control method based on a cognition-based framework to stabilize unknown nonlinear systems. In order to fulfill the task of stabilization, neither the information about the systems dynamical structure nor the knowledge about system physical behaviors, but the system states, which are assumed as measurable, are required. The structure of the proposed controller consists of three parts. The first part is based on a recurrent neural network (RNN) to be used for local identification of the unknown nonlinear system in real time. The network can be utilized as system characteristics, which is further used to design the controller within the third part. In the second part, the set of the given input values leading to stable behavior of the closed-loop system will be calculated numerically with a geometrical criterion based on a suitable definition of quadratic stability. In the third part, a suitable control input value is chosen accordingly to a time-relevant criteria from the set of input values generated in the second part of the controller. These three parts and their internal connections are arranged within a so-called cognition framework. The proposed cognitive controller is able to gain useful knowledge (with local validity) and define autonomously a suitable control input with respect to the requirements of the time-relevant criteria. Numerical examples are shown to demonstrate the successful application and performance of the method. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim) 相似文献
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We analyze a discrete-time network of queues. The unit element of the network is the 2 × 2 buffered switch, which we regard as a system of two queues working in parallel. We show how to transform transition probability information from the output of one switch, or network stage, to the input of the next one. This is used to carry out a Markov time series input model to predict mean queue length at every stage of the system. Another model considered is a renewal process time series model, which we use to find the mean queue length of the second stage of the network. Numerical simulations fall within the narrow band spanned by the two models. 相似文献
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We study the properties of small regulatory networks treated as non-autonomous dynamical systems, otherwise called modules when working inside larger networks. We explicit and study the conditions on the input sequences and the internal parameters of the system to behave as a transducer (finite-state automata with inputs and outputs). In the allowed families of networks, we distinguish those with and without feedback on the basis of whether the internal dynamics of the module has a role on determining their input–output behaviors or not. The input–output and non-autonomous bifurcation analysis of this class of modules rely on studying their symbolic dynamics. We consider the interplay between the internal and structural properties of the modules and the different possible inputs on them to deduce possible new functionalities as internal and external responses. Far from the over-optimistic view according to which to a module shall correspond one functionality, we obtain a trade-off between a large spectrum of behaviors and the robustness of each of them depending on the delays, non-linearities and strengths involved in the regulations. 相似文献